1 Preparation
## load libraries
library(tidyverse)
library(ez)
library(lme4)
library(lmerTest)
# set up the theme for plot and rainclound plot
plot_theme = theme(
text = element_text(size = 10),
axis.title = element_text(size = 16),
axis.text = element_text(size = 14), # the size of the texts in plot
# axis.text.x = element_text(angle = 45, vjust = 0.5),
legend.title=element_text(size=16),
legend.text=element_text(size=16),
legend.position = "right",
legend.key.width = unit(1.2, "cm"),
plot.title = element_text(lineheight=.8, face="bold", size = 17),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid', arrow = arrow(length = unit(0.3, "cm"))),
# remove the facet background color
strip.text = element_text(face="bold", size=12, lineheight=5.0),
strip.background = element_rect(fill="white", colour="white", size=1),
strip.placement = "outside"
)
source(file.path("R", "geom_flat_violin.R"))This R markdown file analyzed the data for the composite face task (complete design) without catch trials. (Experiment number is 104_6)
###### Optional ######
## the filename of the raw data file
fn.raw <- file.path("data","104_106_n-20_2017-05-04-1446.csv")
## input the filename of the output file (.csv)
output.name <- "E1_104_106"The raw data was loaded from the file named 104_106_n-20_2017-05-04-1446.csv in the data/ folder.
2 Tidying up the data
# read data file
df.raw <- read_csv(fn.raw)
str(df.raw)## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 12800 obs. of 21 variables:
## $ Name : num 603 603 603 603 603 603 603 603 603 603 ...
## $ Experiment : chr "104_CF_CFS" "104_CF_CFS" "104_CF_CFS" "104_CF_CFS" ...
## $ Participant : num 603 603 603 603 603 603 603 603 603 603 ...
## $ Trial : num 1 2 3 4 5 6 7 8 9 10 ...
## $ SubTrial : logi NA NA NA NA NA NA ...
## $ Condition : chr "Complete" "Complete" "Complete" "Complete" ...
## $ Congruency : chr "I" "C" "C" "C" ...
## $ Alignment : chr "M" "A" "M" "A" ...
## $ SameDifferent : chr "D" "D" "D" "S" ...
## $ FaceGroup : chr "M3" "F3" "M1" "F4" ...
## $ StudyUpper : chr "M3183.png" "F3047.png" "M1122.png" "F4020.png" ...
## $ StudyLower : chr "M3187.png" "F3062.png" "M1119.png" "F4018.png" ...
## $ TargetUpper : chr "M3153.png" "F3079.png" "M1186.png" "F4020.png" ...
## $ TargetLower : chr "M3187.png" "F3112.png" "M1125.png" "F4018.png" ...
## $ thisResponse : chr "D" "D" "D" "S" ...
## $ isCorrect : num 1 1 1 1 1 1 1 0 1 1 ...
## $ reactionTime : num 0.165 0.231 0.361 0.301 0.176 ...
## $ studyDuration : num 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ...
## $ targetDuration: num 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 ...
## $ maskDuration : num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
## $ trialEndTime : chr "2017-04-03-10:07:07" "2017-04-03-10:07:10" "2017-04-03-10:07:12" "2017-04-03-10:07:15" ...
## - attr(*, "spec")=
## .. cols(
## .. Name = col_double(),
## .. Experiment = col_character(),
## .. Participant = col_double(),
## .. Trial = col_double(),
## .. SubTrial = col_logical(),
## .. Condition = col_character(),
## .. Congruency = col_character(),
## .. Alignment = col_character(),
## .. SameDifferent = col_character(),
## .. FaceGroup = col_character(),
## .. StudyUpper = col_character(),
## .. StudyLower = col_character(),
## .. TargetUpper = col_character(),
## .. TargetLower = col_character(),
## .. thisResponse = col_character(),
## .. isCorrect = col_double(),
## .. reactionTime = col_double(),
## .. studyDuration = col_double(),
## .. targetDuration = col_double(),
## .. maskDuration = col_double(),
## .. trialEndTime = col_character()
## .. )# clean and rename the levels of independent variables
df.clean <- {
df.raw %>%
tbl_df() %>% # change into table data frame format
mutate(
Participant = as.factor(Participant),
Congruency = as.factor(Congruency),
Alignment = as.factor(Alignment),
Experiment = as.factor(Experiment),
Congruency = recode(Congruency, "C" = "congruent", "I" = "incongruent"), # rename the levels for Congruency
Alignment = recode(Alignment, "A" = "aligned", "M" = "misaligned"),
Experiment = recode(Experiment, "104_CF_CFS" = "CFS", "106_CF_Mono" = "monocular")
) %>% # rename the levels for Alignment
group_by(Experiment, Participant, Congruency, Alignment, SameDifferent, isCorrect) %>% # divide into groups
mutate(
RT.Z = scale(reactionTime), # calculate the Z value for response times within each group
RT.Within3Z = ifelse(RT.Z <= 3 & RT.Z >= -3, 1, ifelse(RT.Z < -3 | RT.Z > 3, 0, NaN))
) %>% # if the Z values are between -3 and 3, will be marked as 1.
mutate(Stimuli = paste(StudyUpper, StudyLower, TargetUpper, TargetLower, sep = "-")) %>%
ungroup() # ungroup the data
}
df.clean3 Sensitivity d’
# calculate the d'
individual.d.R <- { # get the hit and correct rejection rates
df.clean %>%
group_by(Experiment, Participant, Congruency, Alignment, SameDifferent) %>% # divide the data into serveral groups
summarize(Accuracy = mean(isCorrect), Count = n()) %>% # get accuracy and count for each condition (hit and correct rejection)
mutate(Accuracy = ifelse(Accuracy == 1, 1 - 0.5/Count, Accuracy)) %>% # set 1 equals to 1-0.5/n
spread(SameDifferent, Accuracy) %>% # spread the Accuracy into two columns based on SameDifferent
mutate(FA = 1 - D,
Z.hit = qnorm(S),
Z.FA = qnorm(FA),
dprime = Z.hit - Z.FA) %>% # get the d prime
select(Experiment, Participant, Congruency, Alignment, dprime, Count)
}
# descriptive statistics of d for plotting
sum.d.R <- {
individual.d.R %>%
group_by(Experiment, Congruency, Alignment) %>%
summarize(Mean = mean(dprime), N = n(), SD = sd(dprime), SE = SD/sqrt(N), SE.hi = Mean + SE, SE.lo = Mean - SE, CI = SE * qt(0.975,N), Median = median(dprime), Lower = Mean - SD, Upper = Mean + SD) %>%
ungroup()
}
# d for SPSS sytle analysis
individual.d.SPSS <- {
individual.d.R %>%
ungroup() %>% # ungroup the data.d.r
mutate(Conditions = paste(Experiment, Congruency, Alignment, sep = ".")) %>% # combine all the levels together
select(Participant, dprime, Conditions, Count) %>% # delete the Independet Variables
spread(Conditions, dprime)
}
# save the d' for R analysis
if (saveCSV) {
write.csv(individual.d.R, output.d.R, row.names = FALSE)
write.csv(individual.d.SPSS, output.d.SPSS, row.names = FALSE)
}3.1 RainClound plot of sensitivity (d’)
knitr::kable(sum.d.R, digits = 4)| Experiment | Congruency | Alignment | Mean | N | SD | SE | SE.hi | SE.lo | CI | Median | Lower | Upper |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CFS | congruent | aligned | 2.5001 | 20 | 0.6983 | 0.1561 | 2.6563 | 2.3440 | 0.3257 | 2.3970 | 1.8019 | 3.1984 |
| CFS | congruent | misaligned | 2.5646 | 20 | 0.5949 | 0.1330 | 2.6976 | 2.4316 | 0.2775 | 2.5899 | 1.9697 | 3.1596 |
| CFS | incongruent | aligned | 2.4831 | 20 | 0.7803 | 0.1745 | 2.6576 | 2.3086 | 0.3640 | 2.4250 | 1.7028 | 3.2634 |
| CFS | incongruent | misaligned | 2.5088 | 20 | 0.6606 | 0.1477 | 2.6565 | 2.3611 | 0.3081 | 2.4161 | 1.8483 | 3.1694 |
| monocular | congruent | aligned | 2.7825 | 20 | 0.5514 | 0.1233 | 2.9058 | 2.6592 | 0.2572 | 2.8591 | 2.2311 | 3.3339 |
| monocular | congruent | misaligned | 2.6769 | 20 | 0.7150 | 0.1599 | 2.8368 | 2.5170 | 0.3335 | 2.8204 | 1.9619 | 3.3920 |
| monocular | incongruent | aligned | 2.1700 | 20 | 0.8822 | 0.1973 | 2.3673 | 1.9727 | 0.4115 | 1.9600 | 1.2878 | 3.0522 |
| monocular | incongruent | misaligned | 2.4848 | 20 | 0.6284 | 0.1405 | 2.6253 | 2.3442 | 0.2931 | 2.4381 | 1.8563 | 3.1132 |
d.RainPlot <- {
ggplot(data = individual.d.R, aes(y = dprime, x = Alignment, fill = Congruency)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
geom_point(aes(y = dprime, color = Congruency), position = position_jitter(width = .15), size = .5, alpha = .8) +
geom_boxplot(aes(y = dprime), width = .2, outlier.shape = NA, alpha = .7) +
geom_point(data = sum.d.R, aes(y = Mean, x = Alignment, color = Congruency), position = position_nudge(x = 0.3), size = 2.5) +
geom_errorbar(data = sum.d.R, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
facet_grid(. ~ Experiment, switch = "both") +
# scale_colour_grey() + # start = .1, end = .6, color for the contour
# scale_fill_grey() + # start = .3, end = .6, color for the fill
# scale_color_brewer(palette = "Set1") +
# scale_fill_brewer(palette = "Set1") +
labs(title = "Sensitivity d' for E104_106", x = "Experiment", y = "Sensitivity", fill = "Congruency", color = "Congruency") + # set the names for main, x and y axises and the legend
# coord_cartesian(ylim = c(0, 6)) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
# geom_hline(yintercept = 0, linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
theme_bw() + # the backgroud color
plot_theme
}## Warning: Ignoring unknown aesthetics: y
d.RainPlot
3.2 Repeated measure ANOVA for sensitivity (d’)
3.2.1 Sensitivity d’: Experiment(2) \(\times\) Congruency(2) \(\times\) Alignment(2)
d.anova <- {
ezANOVA(data = individual.d.R
, dv = dprime
, wid = Participant
, within = c(Experiment, Congruency, Alignment)
, detailed = TRUE
# , type = 3 # for this test, results for type II and III are the same
# , return_aov = TRUE
)
}
knitr::kable(d.anova$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 1017.1601 | 49.3323 | 391.7520 | 0.0000 | * | 0.9325 |
| Experiment | 1 | 19 | 0.0083 | 6.2512 | 0.0251 | 0.8757 | 0.0001 | |
| Congruency | 1 | 19 | 1.9252 | 4.5127 | 8.1056 | 0.0103 | * | 0.0255 |
| Alignment | 1 | 19 | 0.2241 | 3.3359 | 1.2762 | 0.2727 | 0.0030 | |
| Experiment:Congruency | 1 | 19 | 1.3391 | 2.2886 | 11.1175 | 0.0035 | * | 0.0179 |
| Experiment:Alignment | 1 | 19 | 0.0354 | 2.8230 | 0.2382 | 0.6311 | 0.0005 | |
| Congruency:Alignment | 1 | 19 | 0.3641 | 3.2080 | 2.1566 | 0.1583 | 0.0049 | |
| Experiment:Congruency:Alignment | 1 | 19 | 0.5269 | 1.8787 | 5.3283 | 0.0324 | * | 0.0071 |
3.2.2 Sensitivity d’ for CFS: Congruency(2) \(\times\) Alignment(2)
d.anova.CFS <- {
individual.d.R %>%
filter(Experiment == thisExp) %>%
ezANOVA(
dv = dprime,
wid = Participant,
within = .(Congruency, Alignment),
detailed = TRUE,
return_aov = TRUE
) # run repeaed measures ANOVA
}
knitr::kable(d.anova.CFS$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 505.6839 | 25.5391 | 376.2077 | 0.0000 | * | 0.9338 |
| Congruency | 1 | 19 | 0.0265 | 3.5854 | 0.1405 | 0.7119 | 0.0007 | |
| Alignment | 1 | 19 | 0.0407 | 4.5854 | 0.1686 | 0.6860 | 0.0011 | |
| Congruency:Alignment | 1 | 19 | 0.0075 | 2.1384 | 0.0666 | 0.7992 | 0.0002 |
3.2.3 Sensitivity d’ for monocular: Congruency(2) \(\times\) Alignment(2)
d.anova.CFS <- {
individual.d.R %>%
filter(Experiment == thisExp) %>%
ezANOVA(
dv = dprime,
wid = Participant,
within = .(Congruency, Alignment),
detailed = TRUE,
return_aov = TRUE
) # run repeaed measures ANOVA
}
knitr::kable(d.anova.CFS$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 511.4845 | 30.0445 | 323.4605 | 0.0000 | * | 0.9312 |
| Congruency | 1 | 19 | 3.2378 | 3.2158 | 19.1296 | 0.0003 | * | 0.0789 |
| Alignment | 1 | 19 | 0.2188 | 1.5735 | 2.6416 | 0.1206 | 0.0058 | |
| Congruency:Alignment | 1 | 19 | 0.8835 | 2.9483 | 5.6935 | 0.0276 | * | 0.0228 |
3.3 Line plot of sensitivity (d’)
d.ColuPlot = {
ggplot(data = sum.d.R, aes(y = Mean, x = Alignment, color = Congruency, group = Congruency)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point() +
geom_line() + # position = "dodge", alpha = .7
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(. ~ Experiment, switch = "both") +
coord_cartesian(ylim = c(0, 4.5)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(title = "Sensitivity for E104_106", x = "Experiment", y = "Sensitivity", fill = "Congruency") + # set the names for main, x and y axises
scale_fill_grey() + # set the color for columns
geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
plot_theme
}
d.ColuPlot
4 Correct Response Times
individual.rt.R <- {
df.clean %>%
filter(RT.Within3Z == 1) %>% # only keep the RT within 3 Z
filter(isCorrect == 1) %>% # only keep the correct correct
group_by(Experiment, Participant, Congruency, Alignment) %>% # divide the data into serveral groups
summarize(RT = mean(reactionTime) * 1000 + 300, Count = n()) %>% # get RT and count for each condition
ungroup()
}
# descriptive statistics of RT for plotting
sum.rt.R <- {
individual.rt.R %>%
select(-Count) %>%
group_by(Experiment, Congruency, Alignment) %>%
summarize(Mean = mean(RT), N = n(), SD = sd(RT), SE = SD/sqrt(N), SE.hi = Mean + SE, SE.lo = Mean - SE, CI = SE * qt(0.975,N), Median = median(RT), Lower = Mean - SD, Upper = Mean + SD) %>%
ungroup()
}
# RT for SPSS sytle analysis
individual.rt.SPSS <- {
individual.rt.R %>%
mutate(Conditions = paste(Experiment, Congruency, Alignment, sep = ".")) %>%
select(Participant, Conditions, RT) %>%
spread(Conditions, RT)
}
# save the RT for R analysis
if (saveCSV) {
write.csv(individual.rt.R, output.rt.R, row.names = FALSE)
write.csv(individual.rt.SPSS, output.rt.SPSS, row.names = FALSE)
}4.1 RainClound plot of correct response times
knitr::kable(sum.rt.R, digits = 4)| Experiment | Congruency | Alignment | Mean | N | SD | SE | SE.hi | SE.lo | CI | Median | Lower | Upper |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CFS | congruent | aligned | 542.2552 | 20 | 124.4380 | 27.8252 | 570.0804 | 514.4300 | 58.0423 | 525.4149 | 417.8172 | 666.6932 |
| CFS | congruent | misaligned | 548.6668 | 20 | 124.5759 | 27.8560 | 576.5228 | 520.8107 | 58.1067 | 525.4203 | 424.0908 | 673.2427 |
| CFS | incongruent | aligned | 548.0863 | 20 | 137.0902 | 30.6543 | 578.7406 | 517.4320 | 63.9438 | 537.7043 | 410.9960 | 685.1765 |
| CFS | incongruent | misaligned | 537.6101 | 20 | 117.2179 | 26.2107 | 563.8208 | 511.3994 | 54.6746 | 512.8179 | 420.3922 | 654.8280 |
| monocular | congruent | aligned | 562.0456 | 20 | 115.8470 | 25.9042 | 587.9498 | 536.1414 | 54.0352 | 560.2480 | 446.1986 | 677.8926 |
| monocular | congruent | misaligned | 563.0177 | 20 | 122.9075 | 27.4829 | 590.5006 | 535.5347 | 57.3284 | 567.5096 | 440.1102 | 685.9251 |
| monocular | incongruent | aligned | 578.6119 | 20 | 118.7580 | 26.5551 | 605.1669 | 552.0568 | 55.3929 | 587.7466 | 459.8539 | 697.3698 |
| monocular | incongruent | misaligned | 565.0116 | 20 | 122.5508 | 27.4032 | 592.4148 | 537.6084 | 57.1620 | 574.7539 | 442.4608 | 687.5624 |
rt.RainPlot <- {
ggplot(data = individual.rt.R, aes(y = RT, x = Alignment, fill = Congruency)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
geom_point(aes(y = RT, color = Congruency), position = position_jitter(width = .15), size = .5, alpha = .8) +
geom_boxplot(aes(y = RT), width = .2, outlier.shape = NA, alpha = .7) +
geom_point(data = sum.rt.R, aes(y = Mean, x = Alignment, color = Congruency), position = position_nudge(x = 0.3), size = 2.5) +
geom_errorbar(data = sum.rt.R, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
facet_grid(. ~ Experiment, switch = "both") +
# scale_colour_grey() + # start = .1, end = .6, color for the contour
# scale_fill_grey() + # start = .3, end = .6, color for the fill
# scale_color_brewer(palette = "Set1") +
# scale_fill_brewer(palette = "Set1") +
labs(title = "Correct Response Times for E104_106", x = "Alignment", y = "Response Times (ms)", fill = "Congruency", color = "Congruency") + # set the names for main, x and y axises and the legend
# coord_cartesian(ylim = c(0, 6)) + # set the limit for y axis
# scale_y_continuous(expand = c(0, 0)) + # remove the space between columns and x axis
# geom_hline(yintercept = 0, linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
theme_bw() + # the backgroud color
plot_theme
}## Warning: Ignoring unknown aesthetics: y
rt.RainPlot
4.2 Repeated measure ANOVA for response times
4.2.1 Correct RT: Experiment(2) \(\times\) Congruency(2) \(\times\) Alignment(2)
rt.anova <- {
ezANOVA(data = individual.rt.R
, dv = RT
, wid = Participant
, within = c(Experiment, Congruency, Alignment)
, detailed = TRUE
# , type = 3 # for this test, results for type II and III are the same
# , return_aov = TRUE
)
}
knitr::kable(rt.anova$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 4.940184e+07 | 2097279.139 | 447.5489 | 0.0000 | * | 0.9555 |
| Experiment | 1 | 19 | 2.119146e+04 | 150925.609 | 2.6678 | 0.1189 | 0.0091 | |
| Congruency | 1 | 19 | 4.445290e+02 | 3146.660 | 2.6841 | 0.1178 | 0.0002 | |
| Alignment | 1 | 19 | 6.966238e+02 | 10381.039 | 1.2750 | 0.2729 | 0.0003 | |
| Experiment:Congruency | 1 | 19 | 1.414405e+03 | 6115.301 | 4.3945 | 0.0497 | * | 0.0006 |
| Experiment:Alignment | 1 | 19 | 1.833385e+02 | 17006.359 | 0.2048 | 0.6560 | 0.0001 | |
| Congruency:Alignment | 1 | 19 | 2.474343e+03 | 9486.172 | 4.9559 | 0.0383 | * | 0.0011 |
| Experiment:Congruency:Alignment | 1 | 19 | 1.340280e+01 | 8206.872 | 0.0310 | 0.8620 | 0.0000 |
4.2.2 Correct RT for CFS: Congruency(2) \(\times\) Alignment(2)
rt.anova.CFS <- {
individual.rt.R %>%
filter(Experiment == thisExp) %>%
ezANOVA(
dv = RT,
wid = Participant,
within = .(Congruency, Alignment),
detailed = TRUE,
return_aov = TRUE
) # run repeaed measures ANOVA
}
knitr::kable(rt.anova.CFS$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 2.368834e+07 | 1188680.813 | 378.6369 | 0.0000 | * | 0.9515 |
| Congruency | 1 | 19 | 1.365332e+02 | 4449.812 | 0.5830 | 0.4545 | 0.0001 | |
| Alignment | 1 | 19 | 8.260470e+01 | 8670.159 | 0.1810 | 0.6753 | 0.0001 | |
| Congruency:Alignment | 1 | 19 | 1.425980e+03 | 5416.759 | 5.0018 | 0.0375 | * | 0.0012 |
4.2.3 Correct RT for monocular: Congruency(2) \(\times\) Alignment(2)
rt.anova.CFS <- {
individual.rt.R %>%
filter(Experiment == thisExp) %>%
ezANOVA(
dv = RT,
wid = Participant,
within = .(Congruency, Alignment),
detailed = TRUE,
return_aov = TRUE
) # run repeaed measures ANOVA
}
knitr::kable(rt.anova.CFS$ANOVA, digits = 4)| Effect | DFn | DFd | SSn | SSd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1 | 19 | 2.573470e+07 | 1059523.94 | 461.4896 | 0.0000 | * | 0.9592 |
| Congruency | 1 | 19 | 1.722401e+03 | 4812.15 | 6.8006 | 0.0173 | * | 0.0016 |
| Alignment | 1 | 19 | 7.973576e+02 | 18717.24 | 0.8094 | 0.3796 | 0.0007 | |
| Congruency:Alignment | 1 | 19 | 1.061765e+03 | 12276.29 | 1.6433 | 0.2153 | 0.0010 |
4.3 Line plot of correct response times
rt.ColuPlot = {
ggplot(data = sum.rt.R, aes(y = Mean, x = Alignment, color = Congruency, group = Congruency)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point() +
geom_line() + # position = "dodge", alpha = .7
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
coord_cartesian(ylim = c(0, 1100)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
facet_grid(. ~ Experiment, switch = "both") +
labs(title = "Correct Response Times for E104_106", x = "Experiment", y = "Response Times (ms)", fill = "Congruency") + # set the names for main, x and y axises
scale_fill_grey() + # set the color for columns
geom_text(label = c("*", "", "", "", "", "", "", ""), color = "red", size = 6, nudge_y = 30, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
plot_theme
}
rt.ColuPlot
5 Accuracy (Raw Responses)
path.nesi <- "NeSI"
if (TRUE) {
fn.df <- "P101_104_106_clean"
# write_csv(df.clean, file.path(path.nesi, paste0(fn.df, ".csv")))
save(df.clean, file = file.path(path.nesi, paste0(fn.df, ".RData")))
}5.1 Logit Mixed Model for Accuracy
5.1.1 Logit Mixed Model for the CFS condition
# build the full model (run in NeSI)
# glmm.cfs <- glmer(isCorrect ~ Congruency * Alignment +
# (1 + Congruency * Alignment | Participant) +
# (1 + Congruency * Alignment | Stimuli),
# data = filter(df.clean, Experiment == "CFS"),
# family = "binomial",
# verbose = TRUE,
# control=glmerControl(optCtrl=list(maxfun=1e6))
# )
load(file.path(path.nesi, "P101_glmm_cfs.RData"))
print(summary(glmm.cfs), corr=FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isCorrect ~ Congruency * Alignment + (1 + Congruency * Alignment |
## Participant) + (1 + Congruency * Alignment | Stimuli)
## Data: filter(df.clean, Experiment == "CFS")
## Control: glmerControl(optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 4728.1 4890.4 -2340.1 4680.1 6376
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7927 0.2702 0.3276 0.3941 0.9962
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.174823 0.41812
## Congruencyincongruent 0.490772 0.70055
## Alignmentmisaligned 0.021429 0.14639
## Congruencyincongruent:Alignmentmisaligned 0.024358 0.15607
## Participant (Intercept) 0.253672 0.50366
## Congruencyincongruent 0.027074 0.16454
## Alignmentmisaligned 0.006799 0.08245
## Congruencyincongruent:Alignmentmisaligned 0.101687 0.31888
## Corr
##
## -0.26
## -1.00 0.32
## 0.99 -0.14 -0.98
##
## 0.72
## 0.04 -0.62
## -0.57 -0.94 0.79
## Number of obs: 6400, groups: Stimuli, 840; Participant, 20
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 2.126119 0.153662 13.836
## Congruencyincongruent 0.068188 0.161890 0.421
## Alignmentmisaligned 0.005178 0.137994 0.038
## Congruencyincongruent:Alignmentmisaligned 0.011444 0.224957 0.051
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Congruencyincongruent 0.674
## Alignmentmisaligned 0.970
## Congruencyincongruent:Alignmentmisaligned 0.959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## Model failed to converge with max|grad| = 0.131213 (tol = 0.001, component 1)5.1.2 Logit Mixed Model for the monocular condition
# build the full model (run in NeSI)
# glmm.mono <- glmer(isCorrect ~ Congruency * Alignment +
# (1 + Congruency * Alignment | Participant) +
# (1 + Congruency * Alignment | Stimuli),
# data = filter(df.clean, Experiment == "monocular"),
# family = "binomial",
# verbose = TRUE,
# control=glmerControl(optCtrl=list(maxfun=1e6))
# )
load(file.path(path.nesi, "P101_glmm_mono.RData"))
print(summary(glmm.mono), corr=FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isCorrect ~ Congruency * Alignment + (1 + Congruency * Alignment |
## Participant) + (1 + Congruency * Alignment | Stimuli)
## Data: filter(df.clean, Experiment == "monocular")
## Control: glmerControl(optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 4909.5 5071.8 -2430.7 4861.5 6376
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3081 0.2419 0.3206 0.4131 1.4696
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.57180 0.7562
## Congruencyincongruent 0.84361 0.9185
## Alignmentmisaligned 0.07898 0.2810
## Congruencyincongruent:Alignmentmisaligned 0.23091 0.4805
## Participant (Intercept) 0.19127 0.4373
## Congruencyincongruent 0.05704 0.2388
## Alignmentmisaligned 0.03721 0.1929
## Congruencyincongruent:Alignmentmisaligned 0.14926 0.3863
## Corr
##
## -0.84
## -1.00 0.85
## 0.54 0.00 -0.52
##
## 1.00
## 1.00 1.00
## -1.00 -1.00 -1.00
## Number of obs: 6400, groups: Stimuli, 840; Participant, 20
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 2.3245 0.1564 14.858
## Congruencyincongruent -0.5800 0.1590 -3.648
## Alignmentmisaligned -0.1582 0.1570 -1.007
## Congruencyincongruent:Alignmentmisaligned 0.6546 0.2280 2.872
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Congruencyincongruent 0.000264 ***
## Alignmentmisaligned 0.313723
## Congruencyincongruent:Alignmentmisaligned 0.004084 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## Model failed to converge with max|grad| = 0.171893 (tol = 0.001, component 1)6 Versions of Packages Used
sessionInfo()## R version 3.5.2 (2018-12-20)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_New Zealand.1252
## [2] LC_CTYPE=English_New Zealand.1252
## [3] LC_MONETARY=English_New Zealand.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_New Zealand.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 lmerTest_3.0-1 lme4_1.1-19 Matrix_1.2-15
## [5] ez_4.4-0 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.8
## [9] purrr_0.2.5 readr_1.3.1 tidyr_0.8.2 tibble_1.4.2
## [13] ggplot2_3.1.0 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 lubridate_1.7.4 lattice_0.20-38
## [4] assertthat_0.2.0 digest_0.6.18 R6_2.3.0
## [7] cellranger_1.1.0 plyr_1.8.4 backports_1.1.3
## [10] evaluate_0.12 highr_0.7 httr_1.4.0
## [13] pillar_1.3.1 rlang_0.3.0.1 lazyeval_0.2.1
## [16] curl_3.2 readxl_1.2.0 rstudioapi_0.8
## [19] minqa_1.2.4 data.table_1.11.8 nloptr_1.2.1
## [22] car_3.0-2 rmarkdown_1.11 labeling_0.3
## [25] splines_3.5.2 foreign_0.8-71 munsell_0.5.0
## [28] broom_0.5.1 numDeriv_2016.8-1 compiler_3.5.2
## [31] modelr_0.1.2 xfun_0.4 pkgconfig_2.0.2
## [34] mgcv_1.8-26 htmltools_0.3.6 tidyselect_0.2.5
## [37] rio_0.5.16 crayon_1.3.4 withr_2.1.2
## [40] MASS_7.3-51.1 grid_3.5.2 nlme_3.1-137
## [43] jsonlite_1.6 gtable_0.2.0 magrittr_1.5
## [46] scales_1.0.0 zip_1.0.0 cli_1.0.1
## [49] stringi_1.2.4 carData_3.0-2 reshape2_1.4.3
## [52] xml2_1.2.0 generics_0.0.2 openxlsx_4.1.0
## [55] tools_3.5.2 glue_1.3.0 hms_0.4.2
## [58] abind_1.4-5 yaml_2.2.0 colorspace_1.3-2
## [61] rvest_0.3.2 knitr_1.21 bindr_0.1.1
## [64] haven_2.0.0A work by Haiyang Jin